Competition and multi-agent search with overlapping spaces
Investigate competitive and multi-agent versions of the continuum-arm problem-solving model with overlapping search spaces and informational spillovers, and characterize how competitive pressure shapes agents’ equilibrium breadth and depth of search, their selection of approaches, and the resulting balance between duplication and diversity of effort.
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Several avenues for future research remain open. First, examining competitive and multi-agent environments with overlapping search spaces would shed light on the interplay between competitive pressures and what approaches agents choose to pursue. Extending the continuum model to allow for richer informational spillovers across agents could shed light on the balance between duplication and diversity of effort in scientific and technological races.
Although neural networks, in conjunction with classical methods, currently represent the state of the art in gameplaying AI, achieving victory is not the only goal. There are many other open questions in game theory, such as the relation between player performance and complexity of his/her behavior and underlying decision-making processes , the effects of diversity of playing strategies in multiagent gaming environments , , , or the mechanisms of appearance of power laws and memory effects in game-history databases , and in models' training and inference datasets .
So far, a generic multi-agent framework that harnesses structured inter-group competition as the driving evolutionary mechanism for universal photonic design remains elusive.